The article presents a study on the functional brain networks extracted from functional magnetic resonance imaging (fMRI) data. It shows that these networks exhibit scale-free properties, meaning that the distribution of connections follows a power law, with a few highly connected nodes and many less connected ones. These networks also have small-world properties, characterized by short path lengths and high clustering coefficients. The study used fMRI data from human subjects performing various tasks, and found that the networks formed are scale-free, with the degree distribution following a power law. The study also found that the clustering coefficient is much higher than in random networks, and that the average path length is small. The results suggest that the human brain has a complex network structure that is scale-free and small-world. The study also found that the functional networks are robust to changes in parameters and that the results are consistent across different subjects and tasks. The study also compared the results with previous studies on smaller networks and found that the human brain network is the first to show scale-free properties. The study also found that the networks exhibit assortative mixing, where highly connected nodes tend to connect with other highly connected nodes. The study has important implications for understanding brain function and dysfunction, and suggests that the scale-free properties of the brain network may be a general feature of complex systems. The study is supported by grants from the Spanish Ministry of Science and Technology and the US National Institute of Neurological Disorders and Stroke.The article presents a study on the functional brain networks extracted from functional magnetic resonance imaging (fMRI) data. It shows that these networks exhibit scale-free properties, meaning that the distribution of connections follows a power law, with a few highly connected nodes and many less connected ones. These networks also have small-world properties, characterized by short path lengths and high clustering coefficients. The study used fMRI data from human subjects performing various tasks, and found that the networks formed are scale-free, with the degree distribution following a power law. The study also found that the clustering coefficient is much higher than in random networks, and that the average path length is small. The results suggest that the human brain has a complex network structure that is scale-free and small-world. The study also found that the functional networks are robust to changes in parameters and that the results are consistent across different subjects and tasks. The study also compared the results with previous studies on smaller networks and found that the human brain network is the first to show scale-free properties. The study also found that the networks exhibit assortative mixing, where highly connected nodes tend to connect with other highly connected nodes. The study has important implications for understanding brain function and dysfunction, and suggests that the scale-free properties of the brain network may be a general feature of complex systems. The study is supported by grants from the Spanish Ministry of Science and Technology and the US National Institute of Neurological Disorders and Stroke.